Investigation of layered composite plates under acoustic emission using an appropriate finite element model

Sengupta, Sharmila; Roy, Parikshit; Topdar, Pijush; Datta, Asoke Kumar

doi:10.1139/cjce-2020-0452

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Traditional NDT techniques, such as ultrasonics [5,6], acoustic emission [7,8], and thermal imaging [9], primarily focus on detecting material discontinuities, like cracks and defects, to determine the presence of damage within or on the surface of structures. However, these methods have notable limitations, including stringent requirements for the testing environment, restrictions on the size of the structure that can be tested, and high operational costs, rendering them less suitable for marine engineering applications [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Cao,

Liao,

Yan

et al. 2024

Processes

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Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics.

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Section: Introductionmentioning

confidence: 99%

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Cao,

Liao,

Yan

et al. 2024

Processes

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“…5,6 AE method, which is a passive technique, uses elastic stress waves that are released rapidly when a crack initiates and grows. 7,8 This stress wave can be recorded by employing AE sensors over the specimen. AE tomography, damage detection, fracture characterisation, and failure prediction, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet transformation (WT) is a powerful signal processing tool, is also available in the literature and is capable to localise damage or cracks in the structure. 26,27 Previously, researchers used WT as a signal processing tool to detect damage or cracks in a sophisticated structure. An experimental investigation for localising cracks using a WT-based localisation technique was done with different propagation distances, types and depths of AE sources by Zhang et al 16 Kuang et al 28 used the WT-based method for localising cracks on rail heads after noise cancellation.…”

Section: Introductionmentioning

confidence: 99%

Development of a novel real-time AE source localisation technique using ANN for health monitoring of rail section: an experimental study

Kundu

Pal

Roy

et al. 2023

Structural Health Monitoring

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Almost all countries are using railways to transport passengers and freight. Due to heavy load and environmental exposure, these rail sections are prone to damage. Thus, structural health monitoring of the rail section is an essential topic of research to ensure safety and predict the remaining useful life of the track. Consequently, the detection and localisation of damages/cracks in rail section are crucial. It also helps in taking corrective action in time. In this context, the acoustic emission (AE) technique is found to be a powerful non-destructive testing technique which has great potential to localise cracks in rail sections. Actually, the initiation or growth of a damage/crack in a structure emits AE waves, which may be captured using AE sensors mounted on different locations of the structure. The damage itself acts as an AE source. Subsequently, in this study, a novel approach is developed to localise the AE source in a rail section using the artificial neural network (ANN). The ANN model is based on the experimental crack signals captured through a single AE sensor by placing it at different regions of the rail section. For each position of the sensor, cracks are simulated using pencil lead break at different locations along the length and depth of the rail section. It is observed that the developed ANN model predicts the location of the AE source with better accuracy in comparison to the existing localisation methods. The minimum error is found to be less than 1%. Therefore, it can be concluded that the developed ANN model is a suitable option for predicting potential crack locations during real-time monitoring of rail sections.

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“…This model can solve the anisotropy problem in the plate, and it is more widely used. Sengupta et al [ 26 ] proposed a refined 2D plate theory more applicable to plate structures, which satisfies the transverse shear stress continuity at the layer interface. This method has better computational efficiency compared to the 3D plate theory.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

Gao

Wang

et al. 2022

Sensors

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The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

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Investigation of layered composite plates under acoustic emission using an appropriate finite element model

Cited by 5 publications

References 12 publications

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Development of a novel real-time AE source localisation technique using ANN for health monitoring of rail section: an experimental study

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

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